# # coding: utf-8
# import numpy as np
# import matplotlib
# import matplotlib.pyplot as plt
#
# # 设置随机数种子，确保每次运行代码时生成的随机数相同
# np.random.seed(1)
# # 1000 random integers between 0 and 50
# # 生成50个0到99之间的随机整数
# x = np.random.randint(0, 100, 50)
# # Positive Correlation with some noise  # 正相关关系，带有一些噪声（但在这行代码中噪声被注释掉了）
# # y1 = 0.8*x + np.random.normal(0, 15, 50)
# y1 = 0.8*x
# # Negative Correlation with some noise
# y2 = 100 - 0.7*x + np.random.normal(0, 15, 50)
# # No/Weak Correlatio
# y3 = np.random.randint(0, 100, 50)
#
# # 计算x和y1之间的相关系数矩阵
# r1=np.corrcoef(x, y1)
# r2=np.corrcoef(x, y2)
# r3=np.corrcoef(x, y3)
# fig = plt.figure()
#
# plt.subplot(131)
# plt.scatter(x, y1,color='k')        # 第一个子图，展示x和y1的散点图  # 'k'代表黑色
# plt.subplot(132)
# plt.scatter(x, y2,color='k')
# plt.subplot(133)
# plt.scatter(x, y3,color='k')
# print (r1)
# print (r2)
# print (r3)
# plt.show()
# # fig.savefig('./img/correlation1.png',dpi=600)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

# 加载 Iris 数据集
iris = load_iris()
df = pd.DataFrame(data=np.c_[iris['data'], iris['target']], columns=iris['feature_names'] + ['target']).head()

# 假设从数据中提取两列作为新的 x、y1
x = df.iloc[:, 0]  # sepal length (cm)
y1 = df.iloc[:, 1]  # sepal width (cm)

# 计算相关系数矩阵
r1 = np.corrcoef(x, y1)

fig = plt.figure()

plt.subplot(111)
plt.scatter(x, y1, color='k')
print(r1)

plt.show()